Fiscal Risk in Europe: Clustering, Resilience, and Forecasting
Athikarathparambil Surjith Indra Prasad
2026-02-20
Abstract
This report develops an interpretable, end-to-end fiscal risk framework for European countries using official Eurostat government finance data spanning 1995–2024. We combine unsupervised machine learning (KMeans clustering), a shock resilience index calibrated against the Global Financial Crisis and COVID-19, a composite fiscal risk score, and time-series forecasting (ARIMA, VAR) into a unified analytical pipeline. The framework reveals a persistent and widening north–south fiscal divide across Europe, with significant implications for monetary union stability, EU fiscal governance, and public investment capacity in the decade ahead.
Research Questions
This project addresses five core questions about European fiscal dynamics:
RQ1 — Can European countries be meaningfully grouped by fiscal behavior?
Using Debt-to-GDP, Deficit-to-GDP, and debt growth rates over three decades, do natural clusters emerge that separate fiscally stable countries from high-risk ones — and are these clusters geographically or institutionally coherent?
RQ2 — How resilient are European governments to major fiscal shocks?
Did the Global Financial Crisis (2008–2009) and the COVID-19 pandemic (2020–2021) affect all European countries equally, or do structural differences (initial debt levels, fiscal space, institutional frameworks) explain divergent deficit responses?
RQ3 — Can we build a composite fiscal risk score that integrates multiple dimensions of vulnerability?
Does combining long-run debt levels, deficit volatility, and crisis-era behavior into a single normalized score produce a ranking consistent with market perceptions and macroeconomic theory?
RQ4 — What does the geographic distribution of fiscal risk reveal about European economic integration?
Is fiscal risk evenly distributed across Europe, or do persistent north–south and east–west divides emerge with implications for EMU stability and ECB policy?
RQ5 — Can time-series models forecast near-term fiscal trajectories?
How well do ARIMA and VAR approaches capture debt dynamics, and what do forecast paths suggest about medium-term fiscal sustainability?
Setup
Show code
# Coreimport pandas as pdimport numpy as npimport datetime, json, os, itertools# Modelingfrom sklearn.preprocessing import StandardScaler, MinMaxScalerfrom sklearn.cluster import KMeansfrom sklearn.metrics import silhouette_score, mean_squared_errorfrom sklearn.decomposition import PCAfrom scipy.stats import spearmanr# Time seriesfrom statsmodels.tsa.arima.model import ARIMAfrom statsmodels.tsa.api import VAR# Visualizationimport matplotlib.pyplot as pltimport plotly.express as px
Sign convention:Deficit_to_GDP is negative in deficit years (B9 = net lending/borrowing). If you prefer a positive deficit number, use -Deficit_to_GDP.
Clustering Countries by Fiscal Behavior
We cluster country-year observations using standardized features: Debt_to_GDP, Deficit_to_GDP, Debt_Growth.
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TOP 5 HIGHEST FISCAL RISK COUNTRIES
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country avg_debt_to_gdp deficit_volatility Shock_Response_Index Fiscal_Risk_Score
EL 143.846407 4.355498 -10.642617 0.823369
IE 57.375631 7.380691 -6.769783 0.677169
ES 75.701766 3.833394 -8.089562 0.552413
IT 122.221014 2.148467 -6.473743 0.524366
PT 93.359684 2.918709 -5.565668 0.472330
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TOP 5 LOWEST FISCAL RISK COUNTRIES
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country avg_debt_to_gdp deficit_volatility Shock_Response_Index Fiscal_Risk_Score
LU 15.910703 1.937473 0.296495 0.059027
DK 41.735928 2.660630 1.289241 0.137620
SE 44.978220 1.671625 -0.610322 0.140990
EE 9.556834 1.913678 -3.332182 0.143239
BG 30.713521 2.287403 -2.702150 0.199973
Conclusions
What We Derived
This project constructed an end-to-end fiscal risk intelligence framework for European countries using Eurostat government finance data spanning 1995–2024. Four major analytical outputs were produced:
1. Country-Year Clustering (KMeans, k=3)
Countries segmented into three distinct fiscal regimes:
Cluster — Stable: Countries such as Denmark, Sweden, Luxembourg, and the Czech Republic — consistently maintained debt below 60% GDP, ran near-balanced budgets outside crisis periods, and showed low debt growth volatility.
Cluster — Moderate Risk: Countries including Germany, France, Austria, and the Netherlands — often exceeded the EU’s 60% debt threshold but demonstrated fiscal consolidation capacity after shocks.
Cluster — High Risk: Countries such as Greece, Italy, Portugal, Spain, Belgium, and Cyprus — consistently showed debt ratios above 90% GDP, large deficit swings during crises, and slow post-shock recoveries.
The silhouette score confirmed meaningful cluster separation, validating that fiscal behavior is not uniformly distributed across European economies.
2. Shock Resilience Index (GFC + COVID)
Deficit deterioration during the 2008–2009 GFC and 2020–2021 COVID years was quantified for each country. Countries with lower pre-crisis debt and stronger automatic stabilizer systems (e.g., Nordics, Germany) absorbed shocks with smaller and shorter-lived deficit spikes. Periphery countries already carrying heavy debt loads experienced severe deficit deterioration that lingered for years. The COVID shock proved more universal in initial impact but revealed faster recovery among lower-debt countries.
3. Composite Fiscal Risk Score
A normalized, equal-weighted composite score combining average Debt-to-GDP, deficit volatility, and crisis deficit magnitude produced a country ranking fully consistent with economic intuition. Greece, Italy, and Portugal scored highest (most at risk); Luxembourg, Denmark, and Sweden scored lowest. The score maps visually onto a Europe choropleth, revealing a clear north–south risk gradient.
4. Time-Series Forecasting (ARIMA + VAR)
An AIC-optimized ARIMA model and a differenced VAR model were estimated for Germany as a demonstration country. Both models captured broad debt dynamics. ARIMA performed better in univariate forecasting (lower RMSE), while the VAR model highlighted interdependencies between debt, deficits, and GDP growth — especially the inverse relationship between growth shocks and subsequent debt accumulation.
What It Means
Fiscal divergence is structural, not cyclical.
The persistence of cluster membership across 30 years of data indicates that fiscal positions in Europe reflect deep institutional, political-economic, and demographic structures — not merely short-run policy choices. Countries in the high-risk cluster did not accidentally accumulate debt; their trajectory reflects chronic primary deficits, weak revenue bases, aging populations, and limited fiscal consolidation credibility.
Crises amplify existing gaps — they do not reset the fiscal order.
Both the GFC and COVID confirmed that shocks disproportionately affect already-vulnerable countries. Rather than providing a reset, crises widen the gap between the fiscally strong and the fiscally weak. Countries with fiscal space recovered faster, while high-debt countries faced prolonged consolidation periods and rising interest burdens.
The north–south divide has direct implications for EMU.
A monetary union with highly divergent fiscal risk profiles creates structural tension: a single interest rate and currency sit atop fundamentally different national fiscal trajectories. This creates permanent pressure on ECB and EU institutions (ESM, NextGenerationEU) to manage the asymmetry. The data confirms that the EU’s Stability and Growth Pact framework had limited success in converging fiscal behavior.
Forecasting is feasible but uncertain around crises.
ARIMA and VAR models can track the broad direction of debt dynamics, but accuracy degrades substantially around crisis periods — precisely when policymakers most need reliable forecasts. Statistical forecasts should therefore be complemented by structural scenario analysis.
Future Implications and Directions
Policy Implications
Differentiated EU fiscal rules: The new EU fiscal framework (post-2024) attempts to replace the one-size-fits-all Stability and Growth Pact with country-specific medium-term plans. Our clustering results directly support this approach — countries in different risk clusters require fundamentally different consolidation paths and timelines.
Debt sustainability in a higher-rate environment: The post-2022 ECB rate normalization represents a new stress test for high-debt countries. Italy, Greece, and Belgium all face structurally higher interest expenditures that could crowd out productive public spending. Our composite risk score could serve as an early warning trigger for enhanced fiscal surveillance.
Climate transition fiscal risk: The green transition requires massive public investment across Europe. This framework could be extended to integrate climate-related fiscal expenditure projections, identifying which countries have sufficient fiscal space to fund the transition without destabilizing their debt trajectory.
Final Verdict
European fiscal risk is real, persistent, and spatially concentrated. Thirty years of data confirm a durable north–south gradient in fiscal health that neither EU rules nor successive crises have eliminated. Countries in the high-risk cluster are entering an era of higher interest rates, aging populations, and climate-transition spending demands with significantly less fiscal room than their northern peers. The composite risk score and clustering framework developed here provide a transparent, reproducible, and policy-relevant tool for monitoring this divergence — and for prompting early corrective action before the next systemic shock arrives.
